README.md

---
license: mit
tags:
- generated_from_trainer
datasets:
- imdb_urdu_reviews
model-index:
- name: UrduClassification
  results: []
widget:
- text: "میں نے یہ فلم دیکھنے کے لئے بہت احتیاط کی تھی، لیکن اس کی کہانی اور اداکاری نے میری توقعات کو پورا کیا۔ بالکل شاندار فلم!"
  example_title: "Positive Example 1"

- text: "اس فلم کی کہانی بہت بے معنی اور بے چارہ ہے۔ میں نے اپنا وقت اور پیسہ برباد کر دیا۔ براہ کرم اس سے بچیں!"
  example_title: "Negative Example 1"

- text: "یہ ناقابل فہم فلم ہے۔ کوئی بھی اسے دیکھ کر توڑ دل ہو جائے گا۔ بلکل بے فائدہ!"
  example_title: "Negative Example 2"

- text: "میں نے ہمیشہ کی طرح اس فلم کو بھی بہت مزہ دیا۔ اداکاری، کہانی، اور ڈائریکشن سب بہترین تھی۔ دل کھول کر تصویر دیکھنے کا موقع!"
  example_title: "Positive Example 2"

- text: "اس فلم میں اتنی بے وقوفی دکھائی گئی ہے کہ آپ بھی اپنے دماغ کو چیک کریں گے۔ بلکل بکواس!"
  example_title: "Negative Example 3"


  
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# UrduClassification

This model is a fine-tuned version of [urduhack/roberta-urdu-small](https://huggingface.co/urduhack/roberta-urdu-small) on the imdb_urdu_reviews dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4703

## Model Details

- Model Name: Urdu Sentiment Classification
- Model Architecture: RobertaForSequenceClassification
- Base Model: urduhack/roberta-urdu-small
- Dataset: IMDB Urdu Reviews
- Task: Sentiment Classification (Positive/Negative)

## Training Procedure
The model was fine-tuned using the transformers library and the Trainer class from Hugging Face. The training process involved the following steps:

1. Tokenization: The input Urdu text was tokenized using the RobertaTokenizerFast from the "urduhack/roberta-urdu-small" pre-trained model. The texts were padded and truncated to a maximum length of 256 tokens.

2. Model Architecture: The "urduhack/roberta-urdu-small" pre-trained model was loaded as the base model for sequence classification using the RobertaForSequenceClassification class.

3. Training Arguments: The training arguments were set, including the number of training epochs, batch size, learning rate, evaluation strategy, logging strategy, and more.

4. Training: The model was trained on the training dataset using the Trainer class. The training process was performed with gradient-based optimization techniques to minimize the cross-entropy loss between predicted and actual sentiment labels.

5. Evaluation: After each epoch, the model was evaluated on the validation dataset to monitor its performance. The evaluation results, including training loss and validation loss, were logged for analysis.

6. Fine-Tuning: The model parameters were fine-tuned during the training process to optimize its performance on the IMDb Urdu movie reviews sentiment analysis task.

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.4078        | 1.0   | 2500 | 0.3954          |
| 0.2633        | 2.0   | 5000 | 0.4007          |
| 0.1205        | 3.0   | 7500 | 0.4703          |

## Evaluation Results
The model was evaluated on an undisclosed dataset using a language modeling task. The evaluation results after 3 epochs of fine-tuning are as follows:

- Evaluation Loss: 0.3954
- Evaluation Runtime: 51.60 seconds
- Average Samples per Second: 96.89
- Average Steps per Second: 6.06
- Epoch: 3.0

### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3